MOT17Det [1]} and MOT19Det [2]} are datasets for pedestrian detection that are particularly suitable for surveillance applications. They comprise a collection of challenging images (5,316 and 8,931, respectively) taken from multiple sequences with various crowded scenarios having different viewpoints, weather conditions, and camera motions. The authors provided training and test subsets, but they released only the ground-truth annotations belonging to the former. The performance metrics concerning the test subsets are instead available submitting results to their MOT Challenge website (https://motchallenge.net). The main peculiarity of MOT19Det compared to MOT17Det is the massive crowding of the collected scenarios.
